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dc.contributor.authorXu, Qiang
dc.contributor.authorWang, Xin
dc.contributor.authorWang, Junhu
dc.contributor.authorYang, Yajun
dc.contributor.authorFeng, Zhiyong
dc.contributor.editorChen, L
dc.contributor.editorJensen, CS
dc.contributor.editorShahabi, C
dc.contributor.editorYang, X
dc.contributor.editorLian, X
dc.date.accessioned2017-11-29T00:50:56Z
dc.date.available2017-11-29T00:50:56Z
dc.date.issued2017
dc.identifier.issn0302-9743
dc.identifier.doi10.1007/978-3-319-63579-8_12
dc.identifier.urihttp://hdl.handle.net/10072/354614
dc.description.abstractWith the development of the Semantic Web, an increasingly large number of organizations represent their data in RDF format. A single machine cannot efficiently process complex queries on RDF graphs. It becomes necessary to use a distributed cluster to store and process large-scale RDF datasets that are required to be partitioned. In this paper, we propose a semantic-aware partitioning method for RDF graphs. Inspired by the PageRank algorithm, classes in the RDF schema graphs are ranked. A novel partitioning algorithm is proposed, which leverages the semantic information of RDF and reduces crossing edges between different fragments. The extensive experiments on both synthetic and real-world datasets show that our semantic-aware RDF graph partitioning outperforms the state-of-the-art methods by a large margin.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherSpringer
dc.relation.ispartofpagefrom149
dc.relation.ispartofpageto157
dc.relation.ispartofjournalLecture Notes in Computer Science
dc.relation.ispartofvolume10366
dc.subject.fieldofresearchInformation and Computing Sciences not elsewhere classified
dc.subject.fieldofresearchcode089999
dc.titleSemantic-Aware Partitioning on RDF Graphs
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.hasfulltextNo Full Text
gro.griffith.authorWang, John


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